A Multi-Objective Optimization Framework Using a Hybrid GA-HSA Approach for Energy Saving and Reconfiguration in Distribution Systems

This paper describes a novel hybrid evolutionary algorithm for addressing the network reconfiguration problem in distribution systems, with a focus on energy saving.The suggested technique, known as the GA-HSA approach, combines the strengths of the Genetic technique (GA) and the Harmony Search Algorithm.This reconfiguration task's principal objective function is to save energy.Feeder reconfiguration in distribution networks is a nonlinear mixed-integer multi-objective optimization problem in which switches' open/closed statuses are adjusted to move loads from severely laden feeders to less loaded ones.This load transfer helps to save energy and improves the voltage profile of the feeders.Maintaining the radial configuration of the network during reconfiguration is a key constraint and challenge.Furthermore, leveraging the full potential of the algorithm is essential for finding high-quality solutions, particularly for large networks, while minimizing computation time.Conventional GA alone is unsuitable for reconfiguration problems as it risks disrupting the radial structure.Therefore, HSA is employed to reconstruct merged infeasible chromosomes, enhancing GA's limitations.Additionally, a heuristic bi-level codification scheme is utilized, where a secondary level mechanism actively enforces radial constraint compliance.The efficacy of the proposed approach has been validated on four test systems with 16, 33, 69, and 118 buses, showing promising results in terms of efficiency.